Deep learning has become a popular tool for medical image analysis, but the limited
availability of training data remains a major challenge, particularly in the medical field where …

Pre-trained language models (PLMs) have been the de facto paradigm for most natural
language processing tasks. This also benefits the biomedical domain: researchers from …

Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion have attracted
significant attention due to their ability to generate high-quality synthetic images. In this work …

This article discusses the opportunities, applications and future directions of large-scale
pretrained models, ie, foundation models, which promise to significantly improve the …

Various forms of artificial intelligence (AI) applications are being deployed and used in many
healthcare systems. As the use of these applications increases, we are learning the failures …

Foundation models have rapidly permeated society, catalyzing a wave of generative AI
applications spanning enterprise and consumer-facing contexts. While the societal impact of …

Abstract Domain generalization is a ubiquitous challenge for machine learning in
healthcare. Model performance in real-world conditions might be lower than expected …

We present DiffInfinite, a hierarchical diffusion model that generates arbitrarily large
histological images while preserving long-range correlation structural information. Our …

Objective Computer-assisted diagnostic and prognostic systems of the future should be
capable of simultaneously processing multimodal data. Multimodal deep learning (MDL) …

Recently, masked autoencoders have demonstrated their feasibility in extracting effective
image and text features (eg, BERT for natural language processing (NLP) and MAE in …